What does mixed integer programming solve

Mixed-integer linear programming in MATLAB

In MATLAB from MathWorks, developers can now use mixed-integer programming (MILP) for projects. This new solver, which is now available as part of the Optimization Toolbox with the 2014a release, enables users to solve optimization problems that depend on integer or discrete parameters.

With problems that require such solutions, companies can now use integer programming techniques to make optimal decisions. Tools based on MILP can generate significant financial gains and savings, e.g. in portfolio optimization or resource allocation. The new solver can be used in conjunction with MATLAB implementation products to create stand-alone applications based on MILP. In addition, applications based on MILP can be connected with other languages ​​such as Java and .NET.

In many business processes, MILP algorithms help to find solutions that have to assume integer, binary (0 or 1), or otherwise discrete values. For example, the number of wavelengths in a communication network, the "On / Off" status of generators or the specified amount of blocks of shares to be purchased. A MILP solver finds an optimal solution that fulfills all constraints and is usually significantly better than a heuristic solution, such as the rounded solution of a conventional solver.

"Analysts and engineers use MILP to find optimal solutions to common business problems, e.g. portfolio optimization or resource allocation and
Planning, ”said Seth DeLand, Technical Marketing Manager, MathWorks. "By providing the mixed-integer linear programming solver in the Optimization Toolbox, MathWorks enables users to create and deploy decision-making systems based on MILP that can be used across the enterprise."

Optimization Toolbox with MILP solver is now available in Release 2014a (R2014a). For more information, see R2014a Release Highlights.